Phantoms accurately mimicking the electromagnetic and thermal properties of human tissues are essential for the development, characterization, and quality assurance (QA) of clinically used equipment for Hyperthermia Treatment (HT). Currently, a viable recipe for a fat equivalent phantom is not available, mainly due to challenges in the fabrication process and fast deterioration. We propose to employ a glycerol-in-oil emulsion stabilized with ethylcellulose to develop a fat-mimicking material. The dielectric, rheological, and thermal properties of the phantom have been assessed by state-of-the-art measurement techniques. The full-size phantom was then verified in compliance with QA guidelines for superficial HT, both numerically and experimentally, considering the properties variability. Dielectric and thermal properties were proven equivalent to fat tissue, with an acceptable variability, in the 8 MHz to 1 GHz range. The rheology measurements highlighted enhanced mechanical stability over a large temperature range. Both numerical and experimental evaluations proved the suitability of the phantom for QA procedures. The impact of the dielectric property variations on the temperature distribution has been numerically proven to be limited (around 5%), even if higher for capacitive devices (up to 20%). The proposed fat-mimicking phantom is a good candidate for hyperthermia technology assessment processes, adequately representing both dielectric and thermal properties of the human fat tissue while maintaining structural stability even at elevated temperatures. However, further experimental investigations on capacitive heating devices are necessary to better assess the impact of the low electrical conductivity values on the thermal distribution.

Hyperthermia efficacy depends on the temperatures achieved in the target area. Therefore, hyperthermia systems must deliver both controlled and conformal heating. This study presents a comprehensive multi-institutional quality assurance (QA) evaluation of deep hyperthermia devices. Six European institutions equipped with BSD- Sigma 60 and Sigma Eye deep hyperthermia applicators participated in the study. Up to six measurements per applicator were performed in each institution. The thermal distribution in cylindrical homogeneous phantoms after 10 minutes of heating with a total power delivered of 1000 watts was assessed using the applicator's integrated mapping thermometry system. Evaluated quality parameters included temperature increase, focus location, and focus symmetry. A total of 54 measurements were conducted, with 43 included in the analysis. All applicators, except one, achieved a temperature increase of 6 °C in 10 minutes. Central heating capabilities were demonstrated, with mean deviations from the intended location of -1.4 ± 1.6 cm for Sigma 60 and 1.5 ± 1.4 cm for Sigma Eye. Symmetry evaluations showed differences in radial temperature profiles of 6.2 ± 4.5 % for the Sigma 60 and 5.9 ± 4.4 % for the Sigma Eye. We propose minimum acceptable values for each quality parameter based on these results. The measurements were reproducible with acceptable values for the various quality parameters. Potential deviations might be attributed to inaccuracies in the mapping thermometry system rather than the heating system. The presented protocol and practical recommendations should be applied for future QA measurements in deep hyperthermia.

This study aimed to assess the quality of the lucite cone applicator (LCA), the standard applicator for superficial hyperthermia at the Erasmus MC Cancer Institute, using the most recent quality assurance guidelines, thus verifying their feasibility. The assessment was conducted on each of the six LCAs available for clinical treatments. The temperature distribution was evaluated using an infrared camera across different layers of a fat-muscle mimicking phantom. The maximum temperature increase, thermal effective penetration depth (TEPD), and thermal effective field size (TEFS) were used as quality metrics. The experimental results were validated through comparison with simulated results, using a canonical phantom model and a realistic phantom model segmented from CT imaging. A maximum temperature increase above 6 °C at 2 cm depth in the fat-muscle phantom for all the experiments was found. A mean negative difference between simulated and experimental data was of 1.3 °C when using the canonical phantom model. This value decreased to a mean negative difference of 0.4 °C when using the realistic model. Simulated and measured TEPD showed good agreement for both in silico scenarios, while discrepancies were present for TEFS. The LCAs passed all QA guidelines requirements for superficial hyperthermia delivery when used singularly or in an array configuration. A further characterization of parameters such as antenna efficiency and heat transfer coefficients would be beneficial for translating experimental results to simulated values. Implementing the QA guidelines was time-consuming and demanding, requiring careful preparation and correct setup of antenna elements.

Objective: Accurate thermal dose delivery is essential for the clinical success of deep hyperthermia (DHT). As the European Society for Hyperthermic Oncology (ESHO) shifts toward temperature-based QA metrics, standardized tissue-mimicking phantoms for DHT become increasingly important. This study provides quantitative evidence to guide phantom design using computational modeling supported by experimental validation. Approach: Numerical simulations were performed using a simplified model of a clinical DHT applicator operating at 75 and 100 MHz. Parametric studies assessed the influence of phantom geometry (diameter, length, wall thickness) and dielectric properties (relative permittivity and electrical conductivity) on specific absorption rate and temperature distributions. Agreement with patient models was evaluated by comparing phantom simulations with temperature profiles derived from anatomical models. A gel phantom was constructed to validate simulations experimentally. The effects of thermal mapping and positioning errors on thermal profiles were also analyzed. Main results: Simulations demonstrated that dielectric properties, especially electrical conductivity, had the strongest effect on heating patterns and temperature gradients; lower conductivity produced steeper focal profiles and reduced peripheral hotspots. Comparisons with patient models revealed good agreement in the focal region. Experimental measurements matched simulated temperatures near the applicator focus, with an average deviation of 0.7 ± 0.5 °C in the most reproducible series. Larger deviations near boundaries were attributed to thermal mapping uncertainties, catheter bending, and phantom misalignment. Including these uncertainties in the model indicated the need for margins reflecting average positioning errors of ±2.5 cm along the probe axis and ±0.5 cm perpendicular to it. Significance: This study provides a validated modeling framework and design recommendations that support temperature-based QA procedures for DHT. By clarifying how phantom parameters influence measurable temperature profiles and quantifying key sources of experimental uncertainty, this work strengthens the basis for standardized QA phantoms and enhances the reliability of performance assessment for clinical DHT systems.

A type 2 diabetes (T2D) simulator has been recently proposed for supporting drug development and treatment optimization. This tool consists of a physiological model of glucose/insulin/C-peptide dynamics and a virtual cohort of T2D subjects (i.e., random extractions of model parameterizations from a joint parameter distribution) well describing both average and variability realistic T2D dynamics . However, the state-of-art procedure to get a reliable virtual population requires some post-processing after subject extraction, in order to discard implausible behaviors. We propose an improved method for virtual subjects’ generation to overcome this burdensome task. To do so, we first assessed a refined joint parameter distribution, from which extracting a number of subjects, greater than the target population size. Then, target-size subsets are undersampled from the large cohort. The final virtual population is selected among the subsets as the one maximizing the similarity with T2D data and model parameter distribution, by means of measurement’ outcome metrics and Euclidian distance (Δ), respectively. In the final population, almost all the outcome metrics are statistically identical to the clinical counterparts (p-value>0.05) and model parameters’ distribution differs by ~5-10% from that derived from data. The methodology described here is flexible, thus resulting suitable for different T2D stages and type 1 diabetes, as well.Clinical Relevance— A straightforward subjects’ generation would ease the availability of tailored in silico trials for testing diabetes treatment in a specific population.